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Help Us Improve the Wiki
This Wiki is owned by the Home Edge Community. Contributions are always = welcomed to help make it better!
In upper right, select Log In. You will need a Linux Foundation Account = (can be created at http://myprofile.linuxfoundation.org/= a>) to log-in. For a Wiki tutorial, please see Confluence Overview= . Thank you!
Welco= me to the Home Edge Project!
Home Edge is a Growth stage project under the LF Edge umbrella project.&= nbsp; LF Edge aims to establish an open, interoperable framework for edge c= omputing independent of hardware, silicon, cloud, or operating system. = ; Stage 2 projects in LF Edge are i= nterested in reaching the Impact Stage, and have identified a growth plan f= or doing so.
Smart Home has a great potential to enable new business apps thr= ough home edge computing.
Introducing H= ome Edge Project
Home Edge Project, the seed codes will be contributed by Samsung Electro= nics, concentrates on driving and enabling a robust, reliable, and intellig= ent home edge computing open source framework, platform and ecosystem runni= ng on a variety of devices at daily home lives. To accelerate the deploymen= t of the edge computing services ecosystem successfully, the Home Edge Proj= ect will provide users with an interoperable, flexible, and scalable edge c= omputing services platform with a set of APIs that can also run with librar= ies and runtimes.
[1] Home Edge Service : All the de= vices (TVs, fridges, washing machines, etc.) connected into Home Edge Network are cons= idered as Home Edge Devices. All the nodes which need to be part of home edge c= omputing cluster need to run the home edge demon in the background. This de= mon helps to discover devices and their supporting services in the home net= work.
[2] Device Management : Device which acts as gateway can sync data with = cloud interface.
[3] Service Deployment : Application Service like AI Service or Renderin= g service, which are registered with Docker registry can be deployed = in the one or more device in the edge computing cluster.
[4] Service Request : Low end devices request high end devices to perfor= m computing on behalf of them using the scoring manager.
[5] Scoring Manager : Helps to pick a right device to perform service co= mputation using a scoring function(CPU, Memory & Network Bandwidth). If= any appropriate device is not found, the service is run locally on the sam= e device.
[6] Service offload : Low end can delegate the computation the selected = node in home environment.
As an example, on a fig. 1 Home Edge Service runs on the v= arious connected home devices. Camera is one such device, which streams the= images to other device like TV/Mobile in the Home Edge Network. T= his is request to offload image recognition service. The appropri= ate device to offload is based on the scoring value calculated for the vari= ous devices based on processor, memory and bandwidth. The image recognition= service is always maintained to the latest version with adjusted weights t= hrough the cloud sync service. The service deployment as docker container h= elps to deploy the latest service when required. After applications finish = running, Home Edge Service collects the results and presents them = as needed to appropriate device.
Home Edge Platform Architecture